EEG transient event detection and classification using association rules.

Themis P Exarchos; Alexandros T Tzallas; Dimitrios I Fotiadis; Spiros Konitsiotis; Sotirios Giannopoulos

doi:10.1109/titb.2006.872067

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EEG transient event detection and classification using association rules.

Themis P Exarchos·Alexandros T Tzallas·Dimitrios I Fotiadis·Spiros Konitsiotis·Sotirios Giannopoulos

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Abstract

In this paper, a methodology for the automated detection and classification of transient events in electroencephalographic (EEG) recordings is presented. It is based on association rule mining and classifies transient events into four categories: epileptic spikes, muscle activity, eye blinking activity, and sharp alpha activity. The methodology involves four stages: 1) transient event detection; 2) clustering of transient events and feature extraction; 3) feature discretization and feature subset selection; and 4) association rule mining and classification of transient events. The methodology is evaluated using 25 EEG recordings, and the best obtained accuracy was 87.38%. The proposed approach combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules.

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Algorithms Artificial Intelligence Brain Cluster Analysis Computer Simulation Diagnosis, Computer-Assisted Electroencephalography Epilepsy Humans Information Storage and Retrieval Models, Neurological Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity Time Factors Journal Article Research Support, Non-U.S. Gov't

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Themis P Exarchos, Alexandros T Tzallas, Dimitrios I Fotiadis, Spiros Konitsiotis, Sotirios Giannopoulos. 2006. EEG transient event detection and classification using association rules.. https://doi.org/10.1109/titb.2006.872067

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